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archs.py
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archs.py
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import torch
from torch import nn
import torch
import torchvision
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.utils import save_image
from torchvision.datasets import MNIST
import torch.nn.functional as F
import os
import matplotlib.pyplot as plt
from utils import *
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import torch.nn.functional as F
import math
from base_networks import *
__all__ = ['adaptiveunet','priorunet','UNet']
class VGGBlock(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels):
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
self.bn1 = nn.BatchNorm2d(middle_channels)
self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
return out
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=1,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
) -> None:
super().__init__()
norm_layer = nn.BatchNorm2d # Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, 3)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
class UNet(nn.Module):
def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):
super().__init__()
nb_filter = [ 32, 64, 128, 256,512]
self.pool = nn.MaxPool2d(2, 2)
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])
self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])
self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])
self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])
self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])
self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
def forward(self, input):
x0_0 = self.conv0_0(input)
x1_0 = self.conv1_0(self.pool(x0_0))
x2_0 = self.conv2_0(self.pool(x1_0))
x3_0 = self.conv3_0(self.pool(x2_0))
x4_0 = self.conv4_0(self.pool(x3_0))
x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))
x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))
x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))
output = self.final(x0_4)
return output
class priorunet(nn.Module):
def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):
super().__init__()
nb_filter = [32, 64, 128, 256, 512]
self.pool = nn.MaxPool2d(4, 4)
self.up = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])
self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])
self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])
self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])
self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])
self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
self.final = nn.Conv2d(nb_filter[0], input_channels, kernel_size=1)
def forward(self, input):
# pdb.set_trace()
x0_0 = self.conv0_0(input)
x1_0 = self.conv1_0(self.pool(x0_0))
x2_0 = self.conv2_0(self.pool(x1_0))
x3_0 = self.conv3_0(self.pool(x2_0))
x4_0 = self.conv4_0(self.pool(x3_0))
# x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
# x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))
# x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))
# x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))
# output = self.final(x0_4)
# pdb.set_trace()
return x4_0
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
linear = nn.Linear(in_dim, out_dim)
linear.weight.data.normal_()
linear.bias.data.zero_()
self.linear = equal_lr(linear)
def forward(self, input):
return self.linear(input)
class AdaptiveInstanceNorm(nn.Module):
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.BatchNorm2d(in_channel)
self.style = EqualLinear(style_dim, in_channel * 2)
self.style.linear.bias.data[:in_channel] = 1
self.style.linear.bias.data[in_channel:] = 0
def forward(self, input, style):
style = torch.flatten(style, 1)
style = self.style(style).unsqueeze(2).unsqueeze(3)
gamma, beta = style.chunk(2, 1)
out = self.norm(input)
out = gamma * out + beta
return out
class adaptiveunet(nn.Module):
def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):
super().__init__()
nb_filter = [ 32, 64, 128, 256,512]
self.pool = nn.MaxPool2d(2, 2)
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])
self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])
self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])
self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])
self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])
self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
self.adain1_e = AdaptiveInstanceNorm(nb_filter[0], 2048)
self.adain2_e = AdaptiveInstanceNorm(nb_filter[1], 2048)
self.adain3_e = AdaptiveInstanceNorm(nb_filter[2], 2048)
self.adain4_e = AdaptiveInstanceNorm(nb_filter[3], 2048)
self.adain1_d = AdaptiveInstanceNorm(nb_filter[3], 2048)
self.adain2_d = AdaptiveInstanceNorm(nb_filter[2], 2048)
self.adain3_d = AdaptiveInstanceNorm(nb_filter[1], 2048)
self.adain4_d = AdaptiveInstanceNorm(nb_filter[0], 2048)
self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
def forward(self, input, prior):
x0_0 = self.conv0_0(input)
tmp = self.adain1_e(x0_0,prior)
x1_0 = self.conv1_0(self.pool(tmp))
tmp = self.adain2_e(x1_0,prior)
x2_0 = self.conv2_0(self.pool(tmp))
tmp = self.adain3_e(x2_0,prior)
x3_0 = self.conv3_0(self.pool(tmp))
tmp = self.adain4_e(x3_0,prior)
x4_0 = self.conv4_0(self.pool(x3_0))
# pdb.set_trace()
x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
x3_1 = self.adain1_d(x3_1,prior)
x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))
x2_2 = self.adain2_d(x2_2,prior)
x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))
x1_3 = self.adain3_d(x1_3,prior)
x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))
x0_4 = self.adain4_d(x0_4,prior)
output = self.final(x0_4)
return output